The invention as a whole relates to the fields of decision-support software and expert systems.
More specifically, the invention relates to computer software integrating the functions of expert systems and database query systems for executive report analysis.
Current software implementations do not address directly the need for a system integrating (a) report analysis via the generation of diagnostics using an expert system, (b) storage of said diagnostics in a database related to the original information in the reports, and (c) a query system, empowering the user to search for any information in both reports and diagnostics in a coordinated manner.
Heretofore, no instruments have been involved in a similar idea as that dealt with in the invention. However, its individual elements respond to prior art in the following areas: (A) decision-support software and executive information systems, and (B) expert systems, expert system building tools and other artificial intelligence technologies such as neural networks. Other prior art of lesser relevance to this invention includes: (C) programming tools and languages, (D) relational database systems and (E) management information systems.
A. Referring to DECISION-SUPPORT SOFTWARE, existing tools do not address the specific functionality of this invention.
Decision-support software and executive information systems (EISs) have been primarily designed for business managers. These systems allow managers to see very simple summary reports from huge amounts of information stored in databases. Simplicity is foremost in these systems, since managers don't want to deal with menus, and even less with any programming. So, new concepts such as "downdrilling" are introduced. Typically, the first screen the manager will see is a very summarized report. This report could show a few numbers. The manager will want to know more about some number in the screen, so he simply points at that particular number and he will get a more detailed report associated to it. And in this detailed report, he can again do the same, and get another more detailed and specific report. This is called "downdrilling." EISs also provide a programming language and a set of tools for developing simple reports. In other words, EIS systems are for programmers to build simple reporting applications for managers. And then the managers run the EISs with the applications that have already been developed for them.
A few tools will be now discussed as good examples of the current state of prior art.
The DECISION PAD (V.2.0).TM. decision-support tool, by Apian Software of Menlo Park, Calif., combines facts and opinions in spreadsheet-style matrix. Applications include purchasing, employee evaluations, hiring, vendor evaluations, investing, project prioritization, and sales. The product handles up to 250 Alternatives, 250 Criteria, and 60 Evaluators. Weights are by numbers in 1 or 2 levels or by words. Pre-formatted reports include worksheet, bar graphs, sensitivity analysis, scatter plots and logged what-if cases. The product imports and exports files.
The MAXCIM DECISION SUPPORT SYSTEMS II.TM. decision-support tool, by ASK Computer Systems, Inc. of Mountain View, Calif., provides budget upload function supporting file updates, multiple spreadsheet formats, multiple budgets, selective period updating, quarterly spreading and audit trail. The product includes financial report writer with column and row report definitions, user definable formats, financial management reporting and GL integration.
The DECISIONMAKER.TM. decision-support tool, by ASK Computer Systems, Inc. of Mountain View, Calif., gathers information and presents it on screen. The product provides exception reporting; summary information for manufacturing, marketing and financial areas of company; and supporting detail information for specific inquiries.
The EIS.TM. (Executive Information Services.TM.) decision-support tool, by Boeing Computer Services of Seattle, Wash., solves business planning and control problems. Its features include consolidation, modeling, graphics, reports, statistics and financial functions, backward iteration and sensitivity analysis. It is capable of solving simultaneous equations, recording equations, forecasting, estimating, planning, budgeting and performance tracking. It belongs to the class of systems called `authoring systems`.
The Business Insight.TM. decision-support tool, by Business Resource Software of Austin, Tex., is a powerful tool for business analysis, strategic planning, and forecasting. It is capable of providing expert help in the development of promotional strategies, sales methods and inventory management applications. It is related to financial planning systems, spreadsheets and expert systems.
The Decision Support System.TM. (DSS.TM.) decision-support tool, by Definitive Software Inc. of Bloomington, Ind. is an end-user report writing tool. It provides access to computer files and writes reports using menus.
The EIS Tool Kit.TM. decision-support tool, by Executive Performance Systems of Glendale, Ariz., is a complete executive information system (EIS) which includes all programs and drivers necessary to construct complete and functional EIS applications. It enables user to select input and output devices, arrange hierarchy of data to be displayed and customize display screens.
The EIS Toolkit.TM. decision-support tool, by Ferox Microsystems, Inc. of Alexandria, Va., allows the user to build a financial executive information system using his own account structure. This product provides EIS capabilities such as drill-down from summary detail, color exception reporting, trend analysis, graphics and textual annotation of numeric data.
The Executive Information System.TM. decision-support tool, by Global Software, Inc. of Raleigh, N.C., provides icon-based, graphical views of summarized information. It features host logon and delivery of data through scheduled, unattended batch jobs to individual stations. It generates reports through the use of a mouse or with touch screen drivers.
The Forest & Trees.TM. decision-support tool, by Channel Computing Inc. of Newmarket, N.H., is a data access software tool that monitors vital signs of small to large business. It works in the background alerting the user of any unusual data. It provides access and monitoring capabilities to databases and spreadsheets on individual PCs, networked PCs and minicomputers. It provides drill-down capabilities that allow users to access supporting data. It restructures data into visual set of business vital signs that summarize enterprise's status and health. It includes graphics capabilities.
The LightShip.TM. decision-support tool, by Pilot Executive Software, of Boston, Mass., is another popular authoring system for the development of executive information systems.
Of all other executive information systems examined, none provides the specific functionality of this invention. Many of these EIS tools are simply multidimensional spreadsheets, interesting for managers because of the flexibility to recalculate and examine information in different manners and dimensions. A good example of these systems is the CA-COMPUTE.TM. multi-dimensional spreadsheet program, by Computer Associates Corporation of Islandia, N.Y.
B. Referring to EXPERT SYSTEMS and expert systems building tools, it is important to state that Expert systems, commonly known as knowledge systems, have been widely known for at least ten years. However, their technology is an important prior art reference to this invention, so it will be briefly described here.
Researchers define expert systems (also called knowledge systems) in the following way: Intelligent computer programs that use knowledge and inference procedures to solve problems that are hard enough as to require in their solution, significant expertise.
Expert systems typically consist of (a) an interpretive language where the user may write his or her program statements and the conditions associated with those statements, (b) an inference engine, which provides the mechanism through which the expert rules are interpreted and fired, and (c) an executive front-end or expert shell, that helps users write application programs using the language, and that helps them run the expert applications developed, and that also helps them develop and query reports or the generated diagnostics.
With expert systems, in contrast than with other types of programs, users tell the computer what to know, not what to do. To build a traditional program, any developer creates a set of instructions. To build an expert system, the developer creates knowledge. Traditional programs execute by following every step of the algorithms contained in the program. Expert systems execute in different ways.
If an algorithm has to execute step by step to find a solution, that algorithm is really a traditional program. If there isn't a step-by-step method available to solve a particular problem, then artificial intelligence techniques such as expert systems (knowledge systems) and neural networks must be used.
Expert (knowledge) systems contain two basic elements: inference engine and knowledge base. The knowledge base holds all information related to the tasks at hand: (a) the rules and (b) the data on which they will be applied. The inference engine is a mechanism that can operate the information contained in the knowledge base.
In a rule-based system, the knowledge base is divided into a set of rules and working memory (or database).
Just like an IF-THEN sentence, each rule has two parts: a premise and a conclusion. A rule is said to be fired when the inference engine finds the premise is stored as TRUE in working memory (the knowledge base) and it incorporates the conclusion of the rule to the working memory (knowledge base) too.
Working memory is the database contained in the knowledge base. This holds all facts that describe the current situation. Generally, the expert system will start with very few facts. These will expand as the system learns more about the situation at hand, and as far as some rules are executed.
The inference engine or rule interpreter has two tasks. First, it examines facts in working memory and rules in the rule base, and adds new facts to the database (memory) when possible. That is, it fires rules. Second, it determines in what order rules are scanned and fired.
The inference engine can determine the order in which rules should be fired by different methods such as forward chaining, backward chaining, breadth- or depth-wise scan techniques, etc. Applications that use forward chaining, such as process control are called data-driven. Applications that use backward chaining are called goal-driven. Forward chaining should be used for small sets of relevant facts, where many facts lead to few conclusions. A forward chaining system must have all its data at the start, rather than asking the user for information as it goes. Backward chaining should be used for applications having a large set of facts, where one fact can lead to many conclusions. A backward-chaining system will ask for more information if needed to establish a goal.
Some systems' inference engines are designed to ask the user for more information, and to inform the user about the conclusions that have been reached. After the inference engine shows a conclusion to the user, the user may ask "how" and the system will explain how that given value was found. After the inference engine asks a question to the user, the user may also ask "why" and the system will explain why the current question is being asked.
Development of serious expert system implementations is a non-trivial task, usually left to a programmer experienced in the art. Typically, expert system development kits provide a programming language for the development of specific expert applications. A separate element in expert system building tools, commonly known in the industry as an expert shell, also provides the programmer and the user with a front-end application designed to ease the design-program-run cycle in the development and use of an expert system application.
Many expert system building tools are available to the public, such as the VP-EXPERT.TM. expert shell by Paperback Software, the CLIPS.TM. expert system development tool and language by NASA, Nexpert Object.TM. expert system development tools by Neuron Data, and the KnowledgeMaker.TM. development system by Knowledge Garden. On their part, NEURAL NETWORKS attempt to mimic the human brain by "learning" different sets of stimulus patterns (such as medical symptoms) and their associated responses (diagnoses). Incomplete and/or overlapping sets of stimuli can be presented to the neural network, which can then return several responses matching those stimuli using probability weightings to produce an ordered list of responses.
Each neural network problem session contains a set of defined stimuli, a set of defined responses, and a set of relationships between specific groups of stimuli and the response that each group is to produce. The set of stimuli (responses) is represented by a group of stimulus (response) nodes at what is called the "input (output) layer". Then, there is usually one or more intermediate layers, containing nodes that are each linked to every input layer node and every output layer node in the network. The number of the middle layer nodes is usually equal to the average of the number of input and output nodes. Probability values (weights) are then associated with each of these connections and are constantly being updated as the network "learns" new information.
None of the expert systems, expert system building tools or neural network building tools in the prior art are capable of automatically creating an integral database of diagnostics that can be queried simultaneously and in a synchronized manner with the original data. A few existing systems would allow the replication of the operations of this invention using their general-purpose programming languages, but it would be very complicated to program to do the functions equivalent to the claimed invention. To do such a thing would require a fair degree of sophistication in the user, and to do what the claimed invention does is not at all suggested by any prior art expert system or expert system tool, since their teachings are very general in this respect. Only with the benefit of hindsight and a great deal of programming skill would one skilled in the art appreciate that what is done by the claimed invention could be done with the prior art tools and be able to do it. The claimed invention is not obvious from any prior art, because prior art products, tools and theories do not mention building a diagnostics data structure and linking of this structure with the original data along the lines of the claimed invention.
The same could be said of existing languages that are particularly oriented to the development of expert systems other artificial intelligence products and technologies, such as LISP, PROLOG and others.
C. Referring to PROGRAMMING TOOLS AND LANGUAGES, technology and prior art allow the user to easily integrate the operation of independent programs running concurrently, by defining how specific predetermined data should be shared. Good examples of this technology comprise the MICROSOFT WINDOWS.TM. graphical operating environment, by Microsoft Corporation, the NEWWAVE.TM. front-end tool, by Hewlett Packard corporation, the X-WINDOWS.TM. graphical operating environment and the X-MOTIF.TM. graphical operating environment. However, these tools only provide a very general framework where all the programming would still have to be done, if possible, to create a logic procedure equivalent to that of this invention. Furthermore, given the current state of technology and the prior art, it was considered much more practical and easier to develop the best-mode implementation of this invention using from a general-purpose programming language.
D. Referring to relational database systems, a wide number of these systems exist in the market today, but all of these should be interpreted as general-purpose programming languages for the operation of information stored in the relational database format. None of these systems can replicate the operation of this invention without a sizable programming investment.
E. Referring to management information systems (MIS), most current implementations of these systems do not incorporate decision-support tools, expert systems, or an integrated system of generating and querying reports and their associated diagnostics. When a MIS incorporates a decision-support tool, this tool should be interpreted as a separate instrument. MIS systems may integrate data from a whole distributed organization, but nothing has been suggested in the prior art about the generation of a diagnostics database linked to the original data.
As it has already been said, the prior art tools described in sections A to E would be very complicated to program to do a function equivalent to the claimed invention. To do such a thing would require a fair degree of sophistication in the user, and to do what the claimed invention does is not at all suggested by these tools in the prior art. The teachings of the prior art's instruments and programming tools are very general. Only with the benefit of hindsight and a great deal of programming skill would one skilled in the art appreciate that what is done by the claimed invention could be done with the existing programming tools.
The cited PRIOR-ART references are important because the invention provides the benefits of integrating their main characteristics. (A) Like decision--support software and executive information systems, the invention allows managers to see very simple summary reports from huge amounts of information stored in databases. Simplicity is foremost in the invention, as it has been proved in the preferred embodiment implementation. Typical EIS tools such as downdrilling are also used in the invention Typically, the first screen the manager will see is a very summarized report, then the user can get more and more detailed information on those choices he makes. The invention can alert the user of any unusual data. Only very few EIS systems can do this, and they do it only by coloring the data or sounding audible alarms in the computer while the invention builds a whole structured database of diagnostics. Like EIS systems, the invention is capable of restructuring data into visual set of business vital signs that summarize any enterprise's status and health, but the invention will also be able to do this structuring of the information, based on the associated diagnostics' structure. No other system can do this.
(B) Like expert system building tools, the invention allows the definition of expert rules and clauses, and data can be integrated and processed through an inference engine, presenting the final results of the process to the user. Like expert systems, neural networks and other artificial intelligence techniques, the invention is capable of finding solutions to expert analysis problems, summarizing a large body of information into a compact, structured knowledge database.
(C) Like programming tools and languages, the invention allows flexible development of applications in many fields of human knowledge. The expert system--inference engine module in the invention comprises, in fact, a full development programming language.
(D) Like relational database systems, the invention allows data structuring of its basic input information and diagnostic results: relational database indexing, filtering, and data query operations of all types. Full applications can be integrated with the invention through indexing operations between their key files.
(E) Finally, like management information systems, the invention can integrate information of all kinds in a unified environment. The invention is capable of operating this information and presenting appropriate results to managers.
The invention adds a scheme for the integration of data handling benefits and querying techniques from relational database systems, flexible development of applications from programming tools, integration of information from MIS tools, expert analysis from expert systems, and decision support and reporting capabilities from decision support tools. This form of integration that the claimed invention does is not at all suggested by these tools in the prior art.